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Introduction to Cryptography with Open-Source Software (Discrete Mathematics and Its Applications)
by Alasdair McAndrewOnce the privilege of a secret few, cryptography is now taught at universities around the world. Introduction to Cryptography with Open-Source Software illustrates algorithms and cryptosystems using examples and the open-source computer algebra system of Sage. The author, a noted educator in the field, provides a highly practical learning experienc
Introduction to Cyberdeception
by Neil C. Rowe Julian RrushiThis book is an introduction to both offensive and defensive techniques of cyberdeception. Unlike most books on cyberdeception, this book focuses on methods rather than detection. It treats cyberdeception techniques that are current, novel, and practical, and that go well beyond traditional honeypots. It contains features friendly for classroom use: (1) minimal use of programming details and mathematics, (2) modular chapters that can be covered in many orders, (3) exercises with each chapter, and (4) an extensive reference list. Cyberattacks have grown serious enough that understanding and using deception is essential to safe operation in cyberspace. The deception techniques covered are impersonation, delays, fakes, camouflage, false excuses, and social engineering. Special attention is devoted to cyberdeception in industrial control systems and within operating systems. This material is supported by a detailed discussion of how to plan deceptions and calculate their detectability and effectiveness. Some of the chapters provide further technical details of specific deception techniques and their application. Cyberdeception can be conducted ethically and efficiently when necessary by following a few basic principles. This book is intended for advanced undergraduate students and graduate students, as well as computer professionals learning on their own. It will be especially useful for anyone who helps run important and essential computer systems such as critical-infrastructure and military systems.
Introduction to Cybersecurity in the Internet of Things
by Jing Yu Liehuang Zhu Keke GaiThis book addresses the security challenges facing the rapidly growing Internet of Things (IoT) industry, including the potential threats and risks arising from its complex architecture.The authors discuss the overall IoT architecture, covering networking, computing, and security threats and risks to hardware such as sensors, actuators, and portable devices, as well as infrastructure layers. They cover a range of technical concepts such as cryptography, distributed storage, and data transmission, and offer practical advice on implementing security solutions such as authentication and access control.By exploring the future of cybersecurity in the IoT industry, with insights into the importance of big data and the threats posed by data mining techniques, this book is an essential resource for anyone interested in, or working in, the rapidly evolving field of IoT security.
Introduction to Data Analysis and Graphical Presentation in Biostatistics with R
by Thomas W. MacfarlandThrough real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R. These include tools that are critical to dealing with missing data, which is a pressing scientific issue for those engaged in biostatistics. Readers will be equipped to run analyses and make graphical presentations based on the sample dataset and their own data. The hands-on approach will benefit students and ensure the accessibility of this book for readers with a basic understanding of R. Topics include: an introduction to Biostatistics and R, data exploration, descriptive statistics and measures of central tendency, t-Test for independent samples, t-Test for matched pairs, ANOVA, correlation and linear regression, and advice for future work.
Introduction to Data Governance for Machine Learning Systems: Fundamental Principles, Critical Practices, and Future Trends
by Aditya Nandan PrasadThis book is the first comprehensive guide to the intersection of data governance and machine learning (ML) projects. As ML applications proliferate, the quality, reliability, and ethical use of data is central to their success, which gives ML data governance unprecedented significance. However, adapting data governance principles to ML systems presents unique, complex challenges. Author Aditya Nandan Prasad equips you with the knowledge and tools needed to navigate this dynamic landscape effectively. Through this guide, you will learn to implement robust and responsible data governance practices, ensuring the development of sustainable, ethical, and future-proofed AI applications. The book begins by covering fundamental principles and practices of underlying ML applications and data governance before diving into the unique challenges and opportunities at play when adapting data governance theory and practice to ML projects, including establishing governance frameworks, ensuring data quality and interpretability, preprocessing, and the ethical implications of ML algorithms and techniques, from mitigating bias in AI systems to the importance of transparency in models. Monitoring and maintaining ML systems performance is also covered in detail, along with regulatory compliance and risk management considerations. Moreover, the book explores strategies for fostering a data-driven culture within organizations and offers guidance on change management to ensure successful adoption of data governance initiatives. Looking ahead, the book examines future trends and emerging challenges in ML data governance, such as Explainable AI (XAI) and the increasing complexity of data. What You Will Learn Comprehensive understanding of machine learning and data governance, including fundamental principles, critical practices, and emerging challenges Navigating the complexities of managing data effectively within the context of machine learning projects Practical strategies and best practices for implementing effective data governance in machine learning projects Key aspects such as data quality, privacy, security, and ethical considerations, ensuring responsible and effective use of data Preparation for the evolving landscape of ML data governance with a focus on future trends and emerging challenges in the rapidly evolving field of AI and machine learning Who This Book Is For Data professionals, including data scientists, data engineers, AI developers, or data governance specialists, as well as managers or decision makers looking to implement or improve data governance practices for machine learning projects
Introduction to Data Protection Law: Cases and Materials from the EU
by Indranath Gupta Sherin Sarah Philip Paarth NaithaniThis textbook considers cases and materials introducing European Union (EU) data protection law to data protection enthusiasts while acting as a reference point for students and practitioners. The book's utility is twofold: the cases and materials can be used as a textbook as well as reference point for research. The book will benefit individuals new to this area and those at an intermediate level of familiarity with data protection law. It includes judgements delivered by the Court of Justice of the European Union (CJEU)/ European Court of Justice (ECJ) and decisions delivered by Data Protection Authorities (DPA) in the EU. While providing an overview of data protection law in the EU, the book introduces basic concepts of data protection law, principles of data protection law and rights and duties and remedies under the data protection law. Students would greatly benefit from the book by using it as an introduction to data protection law and as a reference point for research and assessments. Course instructors would benefit from the book by using it as core reading material.
Introduction to Data Science
by Laura Igual Santi SeguíThis accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.
Introduction to Data Science in Biostatistics: Using R, the Tidyverse Ecosystem, and APIs
by Thomas W. MacFarlandIntroduction to Data Science in Biostatistics: Using R, the Tidyverse Ecosystem, and APIs defines and explores the term "data science" and discusses the many professional skills and competencies affiliated with the industry. With data science being a leading indicator of interest in STEM fields, the text also investigates this ongoing growth of demand in these spaces, with the goal of providing readers who are entering the professional world with foundational knowledge of required skills, job trends, and salary expectations. The text provides a historical overview of computing and the field's progression to R as it exists today, including the multitude of packages and functions associated with both Base R and the tidyverse ecosystem. Readers will learn how to use R to work with real data, as well as how to communicate results to external stakeholders. A distinguishing feature of this text is its emphasis on the emerging use of APIs to obtain data.
Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science)
by Laura Igual Santi SeguíThis accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or sentiment analysis. Topics and features: Provides numerous practical case studies using real-world data throughout the book Supports understanding through hands-on experience of solving data science problems using Python Describes concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data scienceReviews a range of applications of data science, including recommender systems and sentiment analysis of text data Provides supplementary code resources and data at an associated website This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.
Introduction to Data Systems: Building from Python
by David White Thomas BressoudEncompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form.The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the “data-aptitude” built by the material in this book.
Introduction to Data Technologies (Chapman & Hall/CRC Computer Science & Data Analysis)
by Paul MurrellProviding key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on computational tools, the book shows readers how to improve thei
Introduction to Datafication: Implement Datafication Using AI and ML Algorithms
by Shivakumar R. GoniwadaThis book presents the process and framework you need to transform aspects of our world into data that can be collected, analyzed, and used to make decisions. You will understand the technologies used to gather and process data from many sources, and you will learn how to analyze data with AI and ML models.Datafication is becoming increasingly prevalent in many areas of our lives, from business to education and healthcare. It has the potential to improve decision-making by providing insights into patterns, trends, and correlation between seemingly unconnected pieces of data. This book explains the evolution, principles, and patterns of datafication used in our day-to-day activities. It covers how to collect data from a variety of sources, using technologies such as edge, streaming techniques, REST, and frameworks, as well as data cleansing and data lineage. A data analysis framework is provided to guide you in designing and developing AI and ML projects, including the details of sentiment and behavioral analytics.Introduction to Datafication teaches you how to engineer AI and ML projects by using various methodologies, covers the security mechanisms to be applied for datafication, and shows you how to govern the datafication process with a well-defined governance framework.What You Will LearnUnderstand the principles and patterns to be adopted for dataficationGain techniques for sourcing and mining data, and for sharing data with a data pipelineLeverage the AI and ML algorithms most suitable for dataficationUnderstand the data analysis framework used in every AI and ML projectMaster the details of sentiment and behavioral analytics through practical examplesUtilize development methodologies for datafication engineering and the related security and governance framework Who This Book Is ForStudents, data scientists, data analysts, and AI and ML engineers
Introduction to Deep Learning (Addison-wesley Data And Analytics Ser.)
by Eugene CharniakA project-based guide to the basics of deep learning.This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.
Introduction to Deep Learning Business Applications for Developers
by Armando Vieira Bernardete RibeiroDiscover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples. These applications include image recognition, segmentation and annotation, video processing and annotation, voice recognition, intelligent personal assistants, automated translation, and autonomous vehicles. An Introduction to Deep Learning Business Applications for Developers covers some common DL algorithms such as content-based recommendation algorithms and natural language processing. You’ll explore examples, such as video prediction with fully convolutional neural networks (FCNN) and residual neural networks (ResNets). You will also see applications of DL for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and modeling for financial risk assessment. There will also be mention of the powerful set of algorithms called Generative Adversarial Neural networks (GANs) that can be applied for image colorization, image completion, and style transfer.After reading this book you will have an overview of the exciting field of deep neural networks and an understanding of most of the major applications of deep learning. The book contains some coding examples, tricks, and insights on how to train deep learning models using the Keras framework.What You Will LearnFind out about deep learning and why it is so powerfulWork with the major algorithms available to train deep learning modelsSee the major breakthroughs in terms of applications of deep learning Run simple examples with a selection of deep learning libraries Discover the areas of impact of deep learning in businessWho This Book Is For Data scientists, entrepreneurs, and business developers.
Introduction to Deep Learning for Healthcare
by Cao Xiao Jimeng SunThis textbook presents deep learning models and their healthcare applications. It focuses on rich health data and deep learning models that can effectively model health data. Healthcare data: Among all healthcare technologies, electronic health records (EHRs) had vast adoption and a significant impact on healthcare delivery in recent years. One crucial benefit of EHRs is to capture all the patient encounters with rich multi-modality data. Healthcare data include both structured and unstructured information. Structured data include various medical codes for diagnoses and procedures, lab results, and medication information. Unstructured data contain 1) clinical notes as text, 2) medical imaging data such as X-rays, echocardiogram, and magnetic resonance imaging (MRI), and 3) time-series data such as the electrocardiogram (ECG) and electroencephalogram (EEG). Beyond the data collected during clinical visits, patient self-generated/reported data start to grow thanks to wearable sensors’ increasing use. The authors present deep learning case studies on all data described.Deep learning models: Neural network models are a class of machine learning methods with a long history. Deep learning models are neural networks of many layers, which can extract multiple levels of features from raw data. Deep learning applied to healthcare is a natural and promising direction with many initial successes. The authors cover deep neural networks, convolutional neural networks, recurrent neural networks, embedding methods, autoencoders, attention models, graph neural networks, memory networks, and generative models. It’s presented with concrete healthcare case studies such as clinical predictive modeling, readmission prediction, phenotyping, x-ray classification, ECG diagnosis, sleep monitoring, automatic diagnosis coding from clinical notes, automatic deidentification, medication recommendation, drug discovery (drug property prediction and molecule generation), and clinical trial matching.This textbook targets graduate-level students focused on deep learning methods and their healthcare applications. It can be used for the concepts of deep learning and its applications as well. Researchers working in this field will also find this book to be extremely useful and valuable for their research.
Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence (Undergraduate Topics in Computer Science)
by Sandro SkansiThis textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.
Introduction to Dependent Types with Idris: Encoding Program Proofs in Types
by Boro SitnikovskiDependent types are a concept that allows developers to write proof-carrying code. Idris is a programming language that supports dependent types. This book will teach you the mathematical foundations of Idris as well as how to use it to write software and mathematically prove properties.The first part of the book serves as an introduction to the language's underlying theories. It starts by reviewing formal systems and mathematical logical systems as foundational building blocks, then gradually builds up to dependent types. Next, you'll learn type theory for dependent types. Following this, you'll explore the Idris programming language and conclude by exploring the depths of formal systems and type checkers by implementing them.Introduction to Dependent Types with Idris will walk you through simple examples through more advanced techniques, stepping up the difficulty as you gain more knowledge. Every chapter includes a set of exercises based on what it covered to further cement your learning. No specialized knowledge of mathematics is expected beyond the basics, so it is perfect for novices.What You Will Learn Understand Lambda calculus and dependent types Gain insight into functional programmingWrite mathematical proofs with IdrisWho This Book Is ForProgrammers, mathematicians, academics, and anyone else interested learning dependent types and lambda calculus.
Introduction to DevOps with Chocolate, LEGO and Scrum Game
by Dana PylayevaDiscover a role-based simulation game, designed to introduce DevOps in a very unusual way. Working with LEGO and chocolate, using avatars, personas, and role cards, you will gain an understanding of the Dev and Ops roles as well as their interdependencies. Throughout the game, players go through a range of emotions and learn to expand the boundaries of individual roles, acquire T-shaped skills, and grow the Scrum-team circle to include Operations. The game combines ideas from "The Phoenix Project" with the experience gained from real-life challenges, encountered by development and operations teams in many organizations. Security vulnerabilities, environments patching, deployment code freeze, development and operations silos - the game helps simulate an end-to-end product delivery process and visualize the bottlenecks in the value delivery flow. Introduction to DevOps with Chocolate, LEGO and Scrum Game engages all five senses to maximize learning effectiveness and in three sprints takes players through a gamified DevOps transformation journey. What You Will Learn Play the Chocolate, LEGO and Scrum role-simulation game Gain knowledge of DevOps and how to apply the game to it See how this game illustrates the DevOps cycle as a case study Who This Book Is For Programmers or system admins/project managers who are new to DevOps. DevOps trainers and Agile Coaches who are interested in offering a collaborative and engaging learning experience to their teams.
Introduction to DevOps with Kubernetes: Build scalable cloud-native applications using DevOps patterns created with Kubernetes
by Onur Yılmaz Süleyman AkbaşBecome familiar with Kubernetes and explore techniques to manage your containerized workloads and services Key FeaturesLearn everything from creating a cluster to monitoring applications in KubernetesUnderstand and develop DevOps primitives using KubernetesUse Kubernetes to solve challenging real-life DevOps problemsBook DescriptionKubernetes and DevOps are the two pillars that can keep your business at the top by ensuring high performance of your IT infrastructure. Introduction to DevOps with Kubernetes will help you develop the skills you need to improve your DevOps with the power of Kubernetes. The book begins with an overview of Kubernetes primitives and DevOps concepts. You'll understand how Kubernetes can assist you with overcoming a wide range of real-world operation challenges. You will get to grips with creating and upgrading a cluster, and then learn how to deploy, update, and scale an application on Kubernetes. As you advance through the chapters, you’ll be able to monitor an application by setting up a pod failure alert on Prometheus. The book will also guide you in configuring Alertmanager to send alerts to the Slack channel and trace down a problem on the application using kubectl commands. By the end of this book, you’ll be able to manage the lifecycle of simple to complex applications on Kubernetes with confidence.What you will learnCreate and manage Kubernetes clusters in on-premise systems and cloudExercise various DevOps practices using KubernetesExplore configuration, secret, and storage management, and exercise these on KubernetesPerform different update techniques and apply them on KubernetesUse the built-in scaling feature in Kubernetes to scale your applications up and downUse various troubleshooting techniques and have a monitoring system installed on KubernetesWho this book is forIf you are a developer who wants to learn how to apply DevOps patterns using Kubernetes, then this book is for you. Familiarity with Kubernetes will be useful, but not essential.
Introduction to Digital Communications (Signals and Communication Technology)
by Joachim SpeidelThis book offers students, scientists, and engineers an extensive introduction to the theoretical fundamentals of digital communications, covering single-input single-output (SISO), multiple-input multiple-output (MIMO), and time-variant systems. Further, the main content is supplemented by a wealth of representative examples and computer simulations. The book is divided into three parts, the first of which addresses the principles of wire-line and wireless digital transmission over SISO links. Digital modulation, intersymbol interference, and various detection methods are discussed; models for realistic time-variant, wireless channels are introduced; and the equivalent time-variant baseband system model is derived. This book covers two new topics such as blockwise signal transmission and multicarrier modulation with orthogonal frequency-division multiplexing (OFDM) systems.Since not all readers may be familiar with this topic, Part II is devoted to the theory of linear time-variant systems. The generalized convolution is derived, and readers are introduced to impulse response, the delay spread function, and system functions in the frequency domain. In addition, randomly changing systems are discussed. Several new examples and graphs have been added to this book.In turn, Part III deals with MIMO systems. It describes MIMO channel models with and without spatial correlation, including the Kronecker model. Both linear and nonlinear MIMO receivers are investigated. The question of how many bits per channel use can be transmitted is answered, and maximizing channel capacity is addressed. Principles of space–time coding are outlined in order to improve transmission quality and increase data rates. In closing, the book describes multi-user MIMO schemes, which reduce interference when multiple users in the same area transmit their signals in the same time slots and frequency bands.
Introduction to Digital Control: An Integrated Approach
by Biswanath SamantaThis textbook presents an integrated approach to digital (discrete-time) control systems covering analysis, design, simulation, and real-time implementation through relevant hardware and software platforms. Topics related to discrete-time control systems include z-transform, inverse z-transform, sampling and reconstruction, open- and closed-loop system characteristics, steady-state accuracy for different system types and input functions, stability analysis in z-domain-Jury’s test, bilinear transformation from z- to w-domain, stability analysis in w-domain- Routh-Hurwitz criterion, root locus techniques in z-domain, frequency domain analysis in w-domain, control system specifications in time- and frequency- domains, design of controllers – PI, PD, PID, phase-lag, phase-lead, phase-lag-lead using time- and frequency-domain specifications, state-space methods- controllability and observability, pole placement controllers, design of observers (estimators) - full-order prediction, reduced-order, and current observers, system identification, optimal control- linear quadratic regulator (LQR), linear quadratic Gaussian (LQG) estimator (Kalman filter), implementation of controllers, and laboratory experiments for validation of analysis and design techniques on real laboratory scale hardware modules. Both single-input single-output (SISO) and multi-input multi-output (MIMO) systems are covered. Software platform of Matlab/Simulnik is used for analysis, design, and simulation and hardware/software platforms of National Instruments (NI)/LabVIEW are used for implementation and validation of analysis and design of digital control systems. Demonstrating the use of an integrated approach to cover interdisciplinary topics of digital control, emphasizing theoretical background, validation through analysis, simulation, and implementation in physical laboratory experiments, the book is ideal for students of engineering and applied science across in a range of concentrations.
Introduction to Digital Humanism: A Textbook
by Hannes Werthner Allison Stanger Carlo Ghezzi Jeff Kramer Bashar Nuseibeh Julian Nida-Rümelin Erich PremThis open access textbook introduces and defines digital humanism from a diverse range of disciplines. Following the 2019 Vienna Manifesto, the book calls for a digital humanism that describes, analyzes, and, most importantly, influences the complex interplay of technology and humankind, for a better society and life, fully respecting universal human rights. The book is organized in three parts: Part I “Background” provides the multidisciplinary background needed to understand digital humanism in its philosophical, cultural, technological, historical, social, and economic dimensions. The goal is to present the necessary knowledge upon which an effective interdisciplinary discourse on digital humanism can be founded. Part II “Digital Humanism – a System’s View” focuses on an in-depth presentation and discussion of the main digital humanism concerns arising in current digital systems. The goal of this part is to make readers aware and sensitive to these issues, including e.g. the control and autonomy of AI systems, privacy and security, and the role of governance. Part III “Critical and Societal Issues of Digital Systems” delves into critical societal issues raised by advances of digital technologies. While the public debate in the past has often focused on them separately, especially when they became visible through sensational events the aim here is to shed light on the entire landscape and show their interconnected relationships. This includes issues such as AI and ethics, fairness and bias, privacy and surveillance, platform power and democracy. This textbook is intended for students, teachers, and policy makers interested in digital humanism. It is designed for stand-alone and for complementary courses in computer science, or curricula in science, engineering, humanities and social sciences. Each chapter includes questions for students and an annotated reading list to dive deeper into the associated chapter material. The book aims to provide readers with as wide an exposure as possible to digital advances and their consequences for humanity. It includes constructive ideas and approaches that seek to ensure that our collective digital future is determined through human agency.
Introduction to Digital Image Processing
by William K. PrattThe subject of digital image processing has migrated from a graduate to a junior or senior level course as students become more proficient in mathematical background earlier in their college education. With that in mind, Introduction to Digital Image Processing is simpler in terms of mathematical derivations and eliminates derivations of advanced s
Introduction to Digital Signal Processing Using MATLAB with Application to Digital Communications
by K. S. ThyagarajanThis textbook provides engineering students with instruction on processing signals encountered in speech, music, and wireless communications using software or hardware by employing basic mathematical methods. The book starts with an overview of signal processing, introducing readers to the field. It goes on to give instruction in converting continuous time signals into digital signals and discusses various methods to process the digital signals, such as filtering. The author uses MATLAB throughout as a user-friendly software tool to perform various digital signal processing algorithms and to simulate real-time systems. Readers learn how to convert analog signals into digital signals; how to process these signals using software or hardware; and how to write algorithms to perform useful operations on the acquired signals such as filtering, detecting digitally modulated signals, correcting channel distortions, etc. Students are also shown how to convert MATLAB codes into firmware codes. Further, students will be able to apply the basic digital signal processing techniques in their workplace. The book is based on the author's popular online course at University of California, San Diego.
Introduction to Digital Systems Design
by Giuliano Donzellini Luca Oneto Domenico Ponta Davide AnguitaThis book has been designed for a first course on digital design for engineering and computer science students. It offers an extensive introduction on fundamental theories, from Boolean algebra and binary arithmetic to sequential networks and finite state machines, together with the essential tools to design and simulate systems composed of a controller and a datapath. The numerous worked examples and solved exercises allow a better understanding and more effective learning. All of the examples and exercises can be run on the Deeds software, freely available online on a webpage developed and maintained by the authors. Thanks to the learning-by-doing approach and the plentiful examples, no prior knowledge in electronics of programming is required. Moreover, the book can be adapted to different level of education, with different targets and depth, be used for self-study, and even independently from the simulator. The book draws on the authors’ extensive experience in teaching and developing learning materials.